We provide a more optimized model for calculating credibility score of information in SNS. We premeditated two heuristics which using characteristics of the credibility score for each document: (1) Expertise and (2) unbiasedness. Also, we divide the users in SNS into three types: (1) Creator (2) Distributor, and (3) Follower. Our model is designed to calculate Expertise and Un-biasedness for three types of SNS users (Creator, Distributor, and Follower) by using logistic regression model. Our model not only reveals whether the information is 'accurate and unbiased', but also investigates the 'source, distribution channel, and audience' of the information. We expect our credibility scoring will give answers to the 'qualitative problem' our online world is currently facing.

The analysis of query logs from blog search engines show that news-related queries occupy a significant portion of the logs. This raises a interesting research question on whether the blogosphere can be used to identify important news stories. In this paper, we present novel approaches to identify important news story headlines from the blogosphere for a given day. The proposed system consists of two components based on the language model framework, the query likelihood and the news headline prior. For the query likelihood, we propose several approaches to estimate the query language model and the news headline language model. We also suggest several criteria to evaluate the news headline prior that is the prior belief about the importance or newsworthiness of the news headline for a given day. Experimental results show that our system significantly outperforms a baseline system. Specifically, the proposed approach gives 2.62% and 10.19% further increases in MAP and P@5 over the best performing result of the TREC'09 Top Stories Identification Task.